Exploring the role of sentiment analysis with network and temporal features for finding influential users in social media platforms

Identifying influential nodes within social and weblog networks plays a pivotal role in gaining insights into information dissemination processes and optimizing the structure of social networks to control and accelerate information diffusion. Existing literature on ranking the influence of network nodes encompasses various centrality measures; however, many of these measures perform effectively only under certain conditions, primarily because of lack of a comprehensive ranking mechanism that covers various aspects of a social network. In this study, we have introduced a novel hybrid node ranking model that is tailored for a specific type of social network known as a community weblog. The proposed model integrates classical network centralities with textual and temporal features. One of the significant aspects of the proposed model is the incorporation of temporal features, which assign higher weight to more recent time intervals during social interactions between network nodes. Additionally, the model employed an effective weighting mechanism based on information entropy. The weighting mechanism calibrates appropriate weights for each feature extracted from textual, temporal, and centrality aspects of a weblog network. These features are then objectively weighted and integrated into a hybrid multi-criterion ranking model named CTUserRank. To assign a unified importance rank to nodes in the weblog network, we leveraged the popular and effective aggregation technique known as TOPSIS. Finally, an empirical analysis is performed based on multiple community weblogs to measure the effectiveness of CSTUserRank against classical and recent centrality models. Our findings demonstrate that CSTUserRank outperformed classical and recent centrality models in terms of effectively ranking influential users.